That's why you see other attention free large language models work. But in a world with constraints on time and compute the architecture does matter because scaling does have a cost (which is dropping)
Arguably the most important aspect of transformers is that it allowed easy training of arbitrarily large models.
Also, more effort into completeness needs to go in. Perhaps it is in there, but you just report an "accuracy" without defining what that means. The more I look, the more it starts becoming apparent this likely hasn't received any real human over-sight at all. "What does Grassmann mixing actually buy us?" It buys nothing, because it is a concept, Claude.
Neat idea, but in my opinion devoid of critical thinking / analysis or the level of rigour required to be suitable for broader dissemination. I'd put effort into making this a more credible body of work ASAP. Slop like this WILL cripple your standing in the field.
Agreed.
I say: “if you want a human response, then give human effort”
In my academic career, I had one instance of reviewers actually reading a paper. Otherwise -- accept or reject -- the comments made clear the paper had never been actually read.
Retractions are non-existent, even with clearly proven academic malpractice.
Usually I got quite critical comments (positive and/or negative). Yes, sometimes a reviewer does not write even a single sentence before giving an average score to complete the review process. But this has not been the regular case.
And as a reviewer for a conference paper of 4 to 6 pages, I usually assigned 1 to 3 hours to read it (journal papers: 1 to 2 days) and to give comments.
It should be unsurprising if comments here, made in public, capable of being challenged are far higher quality than official "peer review" which has really become a bit of a joke. Has been for quite a long time, to be fair. (And unsurprising if not, it's a web forum).
Peer reviewed and published means precisely excrement in a world where papers are not retracted and failures to replicate are refused publication by the "prestigious" "peer-reviewed" journals that published the original.
Intelligent, reasoned, knowledgeable review going back and forth with the author in public makes "peer review" seem just as silly as it usually is. Review by peer professor's indentured servants aka PhD students is often actually higher quality, if wildly dishonest and quite frankly disgraceful.
We now live in a world where LLMs can produce papers. Actual research is a little more interesting, with or without LLM. The journal peer review system doesn't seem to be helping.
For AI, the borders seem to have blurred a bit between academic scientists (e.g. at universities) and external scientists at companies or even private people). The problem with the latter is that it is much harder to enforce good scientific practice. Think only about the time it takes to write the state of the art of an ordinary conference paper.
I am also a bit drawn between giving everyone the chance to contribute to research and ensuring good scientific practice. Just writing down your latest ideas and successes is not the same as writing a scientific paper that shows the research in the context of past research and needs, for instance, to be much more mathematically rigorous.
;)
(zt,zt+Delta) where Delta is a positive offset.
This means the model is using the next token to predict the next token. It should have been (zt,zt-Delta), or it's cheating.
I don't know if that's just the paper, or if the code was also b0rk3d.
For each position t and offset Δ∈ such that t+Δ≤L, we form a pair (z_t,z_t+Δ).
For a given t, this produces up to m pairs:
(z_t,z_t+Δ_1),(z_t,z_t+Δ_2),….
We treat these as local neighborhoods at multiple scales. Note that the pairing is causal: we only pair t with positions strictly to its right (future), consistent with left-to-right language modeling.
So z_t is a token in the input, and z_t+Δ is some later token. Guess they could have flipped that around to (z_t-Δ, z_t) with t+Δ>=0.
I think you get what I'm saying, but when they compute the gate they do:
If I'm reading it right g_t = geometric_mix(z_t, z_(t+delta))Which would mean that g_t includes t+Δ, which is a future token. It's leaking future tokens into the current math.
So it could just be a sign error and still be right in the code, or I could just be getting it wrong.
If I'm not though, and given that they got worse results than a standard transformer I really hope it's fixed in the code. If they do worse even when knowing the future it's bad news for the idea.